Methods and apparatus to correct audience measurement data

ABSTRACT

An example involves accessing a unique audience size of database proprietor subscribers exposed to media, the unique audience size of the database proprietor subscribers generated by a process of a server of a database proprietor based on a first quantity of impressions, the first quantity of impressions corresponding to first client devices that include database proprietor identifiers and exclusive of a second quantity of impressions corresponding to second client devices that do not include the database proprietor identifiers; and applying a missing-audience factor to the unique audience size of the database proprietor subscribers exposed to the media to produce a coverage-corrected unique audience size that corrects the unique audience size generated by the server of the database proprietor by using the coverage-corrected unique audience size to represent the first and second client devices in place of the unique audience size that corresponds to the first client devices exclusive of the second client devices.

RELATED APPLICATION

This patent arises from a continuation of U.S. patent application Ser.No. 16/196,481 filed on Nov. 20, 2018, which is a continuation of U.S.patent application Ser. No. 14/502,409 filed on Sep. 30, 2014, now U.S.Pat. No. 10,147,114, and claims priority to U.S. Provisional PatentApplication Ser. No. 61/923,967 filed on Jan. 6, 2014, all of which arehereby incorporated herein by reference in their entireties. Priority toU.S. patent application Ser. No. 16/196,481, U.S. patent applicationSer. No. 14/502,409, and U.S. Provisional Patent Application Ser. No.61/923,967 is claimed.

FIELD OF THE DISCLOSURE

The present disclosure relates generally to monitoring media and, moreparticularly, to methods and apparatus to correct audience measurementdata.

BACKGROUND

Traditionally, audience measurement entities determine audienceengagement levels for media based on registered panel members. That is,an audience measurement entity enrolls people who consent to beingmonitored into a panel. The audience measurement entity then monitorsthose panel members to determine media (e.g., television programs orradio programs, movies, DVDs, advertisements, streaming media, websites,etc.) exposed to those panel members. In this manner, the audiencemeasurement entity can determine exposure metrics for different mediabased on the collected media measurement data.

Techniques for monitoring user access to Internet resources such as webpages, advertisements and/or other Internet-accessible media haveevolved significantly over the years. Some known systems perform suchmonitoring primarily through server logs. In particular, entitiesserving media on the Internet can use known techniques to log the numberof requests received for their media (e.g., content and/oradvertisements) at their server.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates example client devices that report audienceimpressions for internet-based media to impression collection entitiesto facilitate identifying total impressions and sizes of audiencesexposed to different internet-based media.

FIG. 2 illustrates an example communication flow diagram of an examplemanner in which an audience measurement entity (AME) and a databaseproprietor (DP) can collect impressions and demographic informationbased on a client device reporting impressions to the AME and the DP.

FIG. 3 illustrates an example combined impressions table based onimpressions collected by the AME and the DP of FIGS. 1 and 2.

FIG. 4 illustrates example demographic-based unique audience, totalimpressions, and impression frequency data based on impressionscollected by the database proprietor of FIGS. 1 and 2 and havinginaccuracies due to non-coverage of impressions corresponding to clientdevices not having device/user identifiers recognizable by the databaseproprietor.

FIG. 5 illustrates example missing-audience (M-A) factors for differentdemographic groups.

FIG. 6 illustrates coverage-corrected unique audience values andcoverage-corrected impression counts based on the missing-audiencefactors of FIG. 5.

FIG. 7 is a flow diagram representative of machine readable instructionsthat may be executed to implement the coverage corrector of FIG. 2 todetermine the missing-audience factors of FIG. 5 and thecoverage-corrected data of FIG. 6.

FIG. 8 illustrates an example processor system that can be used toexecute the example instructions of FIG. 7 to implement exampleapparatus and/or methods disclosed herein.

DETAILED DESCRIPTION

Techniques for monitoring user access to Internet-accessible media suchas web pages, advertisements, content and/or other media have evolvedsignificantly over the years. At one point in the past, such monitoringwas done primarily through server logs. In particular, entities servingmedia on the Internet would log the number of requests received fortheir media at their server. Basing Internet usage research on serverlogs is problematic for several reasons. For example, server logs can betampered with either directly or via zombie programs which repeatedlyrequest media from the server to increase the server log counts.Secondly, media is sometimes retrieved once, cached locally and thenrepeatedly viewed from the local cache without involving the server inthe repeat viewings. Server logs cannot track these repeat views ofcached media. Thus, server logs are susceptible to both over-countingand under-counting errors.

The inventions disclosed in Blumenau, U.S. Pat. No. 6,108,637,fundamentally changed the way Internet monitoring is performed andovercame the limitations of the server side log monitoring techniquesdescribed above. For example, Blumenau disclosed a technique whereinInternet media to be tracked is tagged with beacon instructions. Inparticular, monitoring instructions are associated with the hypertextmarkup language (HTML) of the media to be tracked. When a clientrequests the media, both the media and the beacon instructions aredownloaded to the client. The beacon instructions are, thus, executedwhenever the media is accessed, be it from a server or from a cache.

The beacon instructions cause monitoring data reflecting informationabout the access to the media to be sent from the client that downloadedthe media to a monitoring entity. Typically, the monitoring entity is anaudience measurement entity (AME) that did not provide the media to theclient and who is a trusted (e.g., neutral) third party for providingaccurate usage statistics (e.g., The Nielsen Company, LLC).Advantageously, because the beaconing instructions are associated withthe media and executed by the client browser whenever the media isaccessed, the monitoring information is provided to the AME irrespectiveof whether the client is a panelist of the AME.

Audience measurement entities and/or other businesses often desire tolink demographics to the monitoring information. To address this issue,the AME establishes a panel of users who have agreed to provide theirdemographic information and to have their Internet browsing activitiesmonitored. When an individual joins the panel, they provide detailedinformation concerning their identity and demographics (e.g., gender,age, ethnicity, income, home location, occupation, etc.) to the AME. Theaudience measurement entity sets a cookie on the panelist computer thatenables the audience measurement entity to identify the panelistwhenever the panelist accesses tagged media and, thus, sends monitoringinformation to the audience measurement entity.

Since most of the clients providing monitoring information from thetagged media are not panelists and, thus, are unknown to the audiencemeasurement entity, it is necessary to use statistical methods to imputedemographic information based on the data collected for panelists to thelarger population of users providing data for the tagged media. However,panel sizes of audience measurement entities remain small compared tothe general population of users. Thus, a problem is presented as to howto increase panel sizes while ensuring the demographics data of thepanel is accurate.

There are many database proprietors operating on the Internet. Thesedatabase proprietors provide services to large numbers of subscribers.In exchange for the provision of the service, the subscribers registerwith the proprietor. As part of this registration, the subscribersprovide detailed demographic information. Examples of such databaseproprietors include social network providers, email providers, etc. suchas Facebook, Myspace, Twitter, Yahoo!, Google, etc. These databaseproprietors set cookies or other device/user identifiers on the clientdevices of their subscribers to enable the database proprietor torecognize the user when they visit their website.

The protocols of the Internet make cookies inaccessible outside of thedomain (e.g., Internet domain, domain name, etc.) on which they wereset. Thus, a cookie set in, for example, the amazon.com domain isaccessible to servers in the amazon.com domain, but not to serversoutside that domain. Therefore, although an audience measurement entitymight find it advantageous to access the cookies set by the databaseproprietors, they are unable to do so.

The inventions disclosed in Mainak et al., U.S. Pat. No. 8,370,489,which is incorporated by reference herein in its entirety, enable anaudience measurement entity to leverage the existing databases ofdatabase proprietors to collect more extensive Internet usage anddemographic data by extending the beaconing process to encompasspartnered database proprietors and by using such partners as interimdata collectors. The inventions disclosed in Mainak et al. accomplishthis task by structuring the AME to respond to beacon requests fromclients (who may not be a member of an audience member panel and, thus,may be unknown to the audience member entity) and redirect the clientfrom the audience measurement entity to a database proprietor such as asocial network site partnered with the audience member entity. Theredirection initiates a communication session between the clientaccessing the tagged media and the database proprietor. The databaseproprietor (e.g., Facebook) can access any cookie it has set on theclient to thereby identify the client based on the internal records ofthe database proprietor. In the event the client corresponds to asubscriber of the database proprietor, the database proprietor logs animpression in association with the demographics data associated with theclient and subsequently forwards logged impressions to the audiencemeasurement company. In the event the client does not correspond to asubscriber of the database proprietor, the database proprietor mayredirect the client to the audience measurement entity and/or anotherdatabase proprietor. The audience measurement entity may respond to theredirection from the first database proprietor by redirecting the clientto a second, different database proprietor that is partnered with theaudience measurement entity. That second database proprietor may thenattempt to identify the client as explained above. This process ofredirecting the client from database proprietor to database proprietorcan be performed any number of times until the client is identified andthe content exposure logged, or until all database partners have beencontacted without a successful identification of the client. Theredirections all occur automatically so the user of the client is notinvolved in the various communication sessions and may not even knowthey are occurring.

Periodically or aperiodically, the partnered database proprietorsprovide their logs and demographic information to the audiencemeasurement entity which then compiles the collected data intostatistical reports accurately identifying the demographics of personsaccessing the tagged media. Because the identification of clients isdone with reference to enormous databases of users far beyond thequantity of persons present in a conventional audience measurementpanel, the data developed from this process is extremely accurate,reliable and detailed.

Significantly, because the audience measurement entity remains the firstleg of the data collection process (e.g., receives the request generatedby the beacon instructions from the client), the audience measuremententity is able to obscure the source of the media access being logged aswell as the identity of the media itself from the database proprietors(thereby protecting the privacy of the media sources), withoutcompromising the ability of the database proprietors to log impressionsfor their subscribers. Further, when cookies are used as device/useridentifiers, the Internet security cookie protocols are complied withbecause the only servers that access a given cookie are associated withthe Internet domain (e.g., Facebook.com) that set that cookie.

Examples disclosed in Mainak et al. (U.S. Pat. No. 8,370,489) can beused to determine any type of media impressions or exposures (e.g.,content impressions, advertisement impressions, content exposure, and/oradvertisement exposure) using demographic information, which isdistributed across different databases (e.g., different website owners,service providers, etc.) on the Internet. Not only do such disclosedexamples enable more accurate correlation of Internet advertisementexposure to demographics, but they also effectively extend panel sizesand compositions beyond persons participating in the panel of anaudience measurement entity and/or a ratings entity to personsregistered in other Internet databases such as the databases of socialmedia sites such as Facebook, Twitter, Google, etc. Such extensioneffectively leverages the media tagging capabilities of the ratingsentity and the use of databases of non-ratings entities such as socialmedia and other websites to create an enormous, demographically accuratepanel that results in accurate, reliable measurements of exposures toInternet media such as advertising and/or programming.

Traditionally, audience measurement entities (also referred to herein as“ratings entities”) determine demographic reach for advertising andmedia programming based on registered panel members. That is, anaudience measurement entity enrolls people that consent to beingmonitored into a panel. During enrollment, the audience measuremententity receives demographic information from the enrolling people sothat subsequent correlations may be made between advertisement/mediaexposures to those panelists and different demographic markets. Unliketraditional techniques in which audience measurement entities relysolely on their own panel member data to collect demographics-basedaudience measurements, example methods, apparatus, and/or articles ofmanufacture disclosed herein enable an audience measurement entity toshare demographic information with other entities that operate based onuser registration models. As used herein, a user registration model is amodel in which users subscribe to services of those entities by creatingan account and providing demographic-related information aboutthemselves. Sharing of demographic information associated withregistered users of database proprietors enables an audience measuremententity to extend or supplement their panel data with substantiallyreliable demographics information from external sources (e.g., databaseproprietors), thus extending the coverage, accuracy, and/or completenessof audience measurement entity's demographics-based audiencemeasurements. Such access also enables the audience measurement entityto monitor persons who would not otherwise have joined an audiencemeasurement panel. Any entity having a network-accessible databaseidentifying demographics of a set of individuals may cooperate with theaudience measurement entity. Such entities may be referred to as“database proprietors” and include entities such as Facebook, Google,Yahoo!, MSN, Twitter, Apple iTunes, Experian, etc.

To increase the likelihood that measured viewership is accuratelyattributed to the correct demographics, examples disclosed herein usedemographic information located in the audience measurement entity'srecords as well as demographic information located at one or moredatabase proprietors that maintain records or profiles of users havingaccounts therewith. In this manner, examples disclosed herein may beused to supplement demographic information maintained by a ratingsentity (e.g., an AME such as The Nielsen Company of Schaumburg, Ill.,United States of America, that collects media exposure measurementsand/or demographics) with demographic information from one or moredifferent database proprietors.

The use of demographic information from disparate data sources (e.g.,high-quality demographic information from the panels of an audiencemeasurement company and/or registered user data of web serviceproviders) results in improved reporting effectiveness of metrics forboth online and offline advertising campaigns. Example techniquesdisclosed herein use online registration data to identify demographicsof users and use server impression counts, tagging (also referred toherein as beaconing), and/or other techniques to track quantities ofimpressions attributable to those users. Online web service providerssuch as social networking sites (e.g., Facebook) and multi-serviceproviders (e.g., Yahoo!, Google, Experian, etc.) (collectively andindividually referred to herein as database proprietors) maintaindetailed demographic information (e.g., age, gender, geographiclocation, race, income level, education level, religion, etc.) collectedvia user registration processes. As used herein, an impression isdefined to be an event in which a home or individual is exposed tocorresponding media (e.g., content and/or an advertisement). Thus, animpression represents a home or an individual having been exposed tomedia (e.g., an advertisement, content, a group of advertisements and/ora collection of content). In Internet advertising, a quantity ofimpressions or impression count is the total number of times media(e.g., content, an advertisement or advertisement campaign) has beenaccessed by a web population (e.g., the number of times the media isaccessed). As used herein, a demographic impression is defined to be animpression that is associated with a characteristic (e.g., a demographiccharacteristic) of the person exposed to the media.

Although such techniques for collecting media impressions are based onhighly accurate demographic information, in some instances impressionscollected by a database proprietor (e.g., Facebook, Yahoo, Google, etc.)may be inaccurate and/or incomplete when the database proprietor doesnot have complete coverage of device/user identifiers (e.g., cookies) atall of the client devices that visit a site of the database proprietor.As used herein in this context, coverage represents the extent to whicha database proprietor has set device/user identifiers in client devicesthat visit the site of the database proprietor. For example, if only 50%of client devices that visit the site of the database proprietor have acookie set therein by the database proprietor, then the databaseproprietor has 50% coverage of client devices that visit its site. Aclient device may not have a cookie set by the database proprietor inits web browser if, for example, a user doesn't have an account with thedatabase proprietor or if the user has an account with the databaseproprietor but has cleared the cookie cache and deleted the databaseproprietor's cookie before or at the time of a media exposure. In suchinstances, the database proprietor would not be able to detect the mediaexposure and, thus, would not report any audience or impressions forthat exposure. As a result, the database proprietor would underestimatethe reach and gross rating points (GRPs) of a campaign.

As used herein, reach is a measure indicative of the demographiccoverage achieved by media such as content or an ad campaign (e.g.,demographic group(s) and/or demographic population(s) exposed to themedia). For example, an ad campaign reaching a broader demographic basewill have a larger reach than an ad campaign that reached a more limiteddemographic base. The reach metric may be measured by tracking mediaimpressions for known users (e.g., panelists or non-panelists) for whichan AME stores demographic information or can obtain demographicinformation (e.g., via a database proprietor).

In illustrated examples disclosed herein, media exposure is measured interms of online Gross Rating Points. A Gross Rating Point (GRP) is aunit of measurement of audience size that has traditionally been used inthe television ratings context. It is used to measure exposure to one ormore media (e.g., programs, advertisements, etc.) without regard tomultiple exposures of the same media to individuals. In terms oftelevision (TV) advertisements, one GRP is equal to 1% of TV households.While GRPs have traditionally been used as a measure of televisionviewership, examples disclosed herein may be used in connection withgenerating online GRPs for online media to provide a standardized metricthat can be used across the Internet to accurately reflect onlineadvertisement exposure. Such standardized online GRP measurements canprovide greater certainty to advertisers that their online advertisementmoney is well spent. It can also facilitate cross-medium comparisonssuch as viewership of TV advertisements and online advertisements,exposure to radio advertisements and online media, etc. Because examplesdisclosed herein may be used to correct impressions that associateexposure measurements with corresponding demographics of users, theinformation processed using examples disclosed herein may also be usedby advertisers to more accurately identify markets reached by theiradvertisements and/or to target particular markets with futureadvertisements.

Examples disclosed herein may be implemented by an audience measuremententity (AME) (e.g., any entity interested in measuring or trackingaudience exposures to advertisements, content, and/or any other media)in cooperation with any number of database proprietors such as onlineweb services providers. Such database proprietors/online web servicesproviders may be social network sites (e.g., Facebook, Twitter, MySpace,etc.), multi-service sites (e.g., Yahoo!, Google, Axiom, Catalina,etc.), online retailer sites (e.g., Amazon.com, Buy.com, etc.), creditreporting sites (e.g., Experian) and/or any other web service(s) sitethat maintains user registration records.

Example processes to adjust impressions collected by databaseproprietors having limited coverage of cookies are disclosed herein. Insome examples, profile correction (e.g., a current Decision Tree (DT)model) is applied to impression data collected by database proprietors.In the illustrated examples, using a panel of registered audiencemembers, an AME calculates missing-adjustment (M-A) factors for eachwebsite and demographic group using, for example, three months ofhistorical data. However, any other amount of historical data may beused. In some examples, the AME panel of registered audience members maybe a cross-platform home television/computer panel (e.g., a TVPC panel).In other examples, the AME panel of registered users may be a computerpanel or internet-device panel without corresponding to a televisionaudience panel. In other examples, the AME panel may be a cross-platformradio/internet panel, and/or a panel focusing on other mediums.

In examples disclosed herein, the AME determines missing-audience (M-A)factors using historical impression data and subsequently applies theM-A factors to impression data collected by a database proprietor inorder to compensate the database proprietor impressions for the databaseproprietor's non-coverage due to not having database proprietordevice/user identifiers set on some client devices. In examplesdisclosed herein, the AME panel includes audience members recruited bythe AME. When recruited into the AME panel, the AME collects demographicinformation from the enrolled panelist audience members so that the AMEcan correlate demographics with exposures to online media. In someexamples, the AME also uses the AME panel demographics data to correlatethe demographics with media exposures (e.g., television exposures, radioexposures, etc.). In some examples, the AME uses particular techniqueswhen collecting panelist demographic information to ensure that thedemographic information is highly accurate. In this manner,demographic-based impression data generated by the AME can accuratelyreflect demographics that correspond to impressions for particularmedia.

Example methods and computer readable instructions disclosed herein maybe used to determine a missing-audience factor based on a first quantityof impressions corresponding to first client devices that do not havedatabase proprietor identifiers for use by a database proprietor toidentify subscribers registered with the database proprietor, and basedon a second quantity of impressions corresponding to second clientdevices that do have the database proprietor identifiers for use by thedatabase proprietor to identify subscribers registered with the databaseproprietor. In such examples, the first quantity of impressions isrepresentative of accesses to media via the first client devices, andthe second quantity of impressions is representative of access to themedia via the second client devices. In such examples, acoverage-corrected unique audience size may be determined based on themissing-audience factor and a unique audience size of databaseproprietor subscribers exposed to the media. In such examples, theunique audience size is determined based on a quantity of impressionslogged by the database proprietor. In such examples, thecoverage-corrected unique audience size corresponds to the quantity ofimpressions logged by the database proprietor and a quantity ofimpressions not logged by the database proprietor. In some examples, themissing-audience factor and the coverage-corrected unique audience sizeare determined by an audience measurement entity separate from thedatabase proprietor. In some examples, the database proprietor is atleast one of a social network service provider or an email serviceprovider.

In some examples, a coverage-corrected impression count is determinedbased on the coverage-corrected unique audience size and an impressionsfrequency. In such some examples, the coverage-corrected impressioncount is representative of the quantity of impressions logged by thedatabase proprietor and the quantity of impressions not logged by thedatabase proprietor. In some examples, the impressions frequency is thequantity of impressions logged by the database proprietor divided by theunique audience size of database proprietor subscribers.

In some examples, the missing-audience factor is determined by dividingthe first quantity of impressions corresponding to the first clientdevices that do not have the database proprietor identifiers by thesecond quantity of impressions corresponding to the second clientdevices that do have the database proprietor identifiers. In someexamples, the missing-audience factor and the coverage-corrected uniqueaudience size are determined based on impressions logged by the databaseproprietor for a particular demographic group identified by the databaseproprietor.

In some examples, the impressions are collected by responding to beaconrequests from client devices by redirecting the client devices tocommunicate with the database proprietor to enable the databaseproprietor to record the impressions. In some such examples, the clientdevices are instructed to provide an identifier (e.g., a device/useridentifier 227 of FIG. 2) to the database proprietor. In such examples,the identifier does not identify the media or a source of the media.

Example apparatus disclosed herein may include a missing-audience factordeterminer to determine a missing-audience factor based on a firstquantity of impressions corresponding to first client devices that donot have database proprietor identifiers for use by a databaseproprietor to identify subscribers registered with the databaseproprietor, and based on a second quantity of impressions correspondingto second client devices that do have the database proprietoridentifiers for use by the database proprietor to identify subscribersregistered with the database proprietor. The first quantity ofimpressions is representative of accesses to media via the first clientdevices. The second quantity of impressions is representative ofaccesses to the media via the second client devices. Disclosed exampleapparatus may also include a unique audience size corrector to determinea coverage-corrected unique audience size based on the missing-audiencefactor and a unique audience size of database proprietor subscribersexposed to the media. In such examples, the unique audience size isdetermined based on a quantity of impressions logged by the databaseproprietor. In such examples, the coverage-corrected unique audiencesize corresponds to the quantity of impressions logged by the databaseproprietor and a quantity of impressions not logged by the databaseproprietor. In some examples, the missing-audience factor determiner andthe unique audience corrector are operated by an audience measuremententity separate from the database proprietor. In some examples, thedatabase proprietor is at least one of a social network service provideror an email service provider.

Some example apparatus also include an impressions corrector todetermine a coverage-corrected impression count based on thecoverage-corrected unique audience size and an impressions frequency. Insuch some examples, the coverage-corrected impression count isrepresentative of the quantity of impressions logged by the databaseproprietor and the quantity of impressions not logged by the databaseproprietor. In some examples, the impressions frequency is the quantityof impressions logged by the database proprietor divided by the uniqueaudience size of database proprietor subscribers.

In some example apparatus, the missing-audience factor determinerdetermines the missing-audience factor by dividing the first quantity ofimpressions corresponding to the first client devices that do not havethe database proprietor identifiers by the second quantity ofimpressions corresponding to the second client devices that do have thedatabase proprietor identifiers. In some example apparatus, themissing-audience factor determiner determines the missing-audiencefactor and the unique audience corrector determines thecoverage-corrected unique audience size based on impressions logged bythe database proprietor for a particular demographic group identified bythe database proprietor.

Some example apparatus also include an impressions collector to collectthe impressions by responding to beacon requests from client devices toredirect the client devices to communicate with the database proprietorto enable the database proprietor to record the impressions. In suchsome examples, the impressions collector instructs the client devices toprovide an identifier (e.g., a device/user identifier 227 of FIG. 2) tothe database proprietor. In such examples, the identifier does notidentify the media or a source of the media.

FIG. 1 illustrates example client devices 102 that report audienceimpressions for internet-based media to impression collection entities104 to facilitate identifying total impressions and sizes of audiencesexposed to different internet-based media. As used herein, the termimpression collection entity refers to any entity that collectsimpression data such as, for example, AMEs and database proprietors thatcollect impression data. The client devices 102 of the illustratedexample may be any device capable of accessing media over a network. Forexample, the client devices 102 may be a computer, a tablet, a mobiledevice, a smart television, or any other Internet-capable device orappliance. Examples disclosed herein may be used to collect impressioninformation for any type of media including content and/oradvertisements. Media may include advertising and/or content deliveredvia web pages, streaming video, streaming audio, internet protocoltelevision (IPTV), movies, television, radio and/or any other vehiclefor delivering media. In some examples, media includes user-generatedmedia that is, for example, uploaded to media upload sites such asYouTube and subsequently downloaded and/or streamed by one or more otherclient devices for playback. Media may also include advertisements.Advertisements are typically distributed with content (e.g.,programming). Traditionally, content is provided at little or no cost tothe audience because it is subsidized by advertisers that pay to havetheir advertisements distributed with the content. As used herein,“media” refers collectively and/or individually to content and/oradvertisement(s).

In the illustrated example, the client devices 102 employ web browsersand/or applications (e.g., apps) to access media, some of which includeinstructions that cause the client devices 102 to report mediamonitoring information to one or more of the impression collectionentities 104. That is, when a client device 102 of the illustratedexample accesses media, a web browser and/or application of the clientdevice 102 executes instructions in the media to send a beacon requestor impression request 108 to one or more impression collection entities104 via, for example, the Internet 110. The beacon requests 108 of theillustrated example include information about accesses to media at thecorresponding client device 102 generating the beacon requests. Suchbeacon requests allow monitoring entities, such as the impressioncollection entities 104, to collect impressions for different mediaaccessed via the client devices 102. In this manner, the impressioncollection entities 104 can generate large impression quantities fordifferent media (e.g., different content and/or advertisementcampaigns).

The impression collection entities 104 of the illustrated exampleinclude an example audience measurement entity (AME) 114 and an exampledatabase proprietor (DP) 116. In the illustrated example, the AME 114does not provide the media to the client devices 102 and is a trusted(e.g., neutral) third party (e.g., The Nielsen Company, LLC) forproviding accurate media access statistics. In the illustrated example,the database proprietor 116 is one of many database proprietors thatoperates on the Internet to provide services to large numbers ofsubscribers. Such services may be email services, social networkingservices, news media services, cloud storage services, streaming musicservices, streaming video services, online retail shopping services,credit monitoring services, etc. Example database proprietors includesocial network sites (e.g., Facebook, Twitter, MySpace, etc.),multi-service sites (e.g., Yahoo!, Google, etc.), online retailer sites(e.g., Amazon.com, Buy.com, etc.), credit services (e.g., Experian),and/or any other web service(s) site that maintains user registrationrecords. In examples disclosed herein, the database proprietor 116maintains user account records corresponding to users registered forInternet-based services provided by the database proprietors. That is,in exchange for the provision of services, subscribers register with thedatabase proprietor 116. As part of this registration, the subscribersprovide detailed demographic information to the database proprietor 116.Demographic information may include, for example, gender, age,ethnicity, income, home location, education level, occupation, etc. Inthe illustrated example, the database proprietor 116 sets a device/useridentifier (e.g., an identifier described below in connection with FIG.2) on a subscriber's client device 102 that enables the databaseproprietor 116 to identify the subscriber.

In the illustrated example, when the database proprietor 116 receives abeacon/impression request 108 from a client device 102, the databaseproprietor 116 requests the client device 102 to provide the device/useridentifier that the database proprietor 116 had previously set for theclient device 102. The database proprietor 116 uses the device/useridentifier corresponding to the client device 102 to identifydemographic information in its user account records corresponding to thesubscriber of the client device 102. In this manner, the databaseproprietor 116 can generate demographic impressions by associatingdemographic information with an audience impression for the mediaaccessed at the client device 102. As explained above, a demographicimpression is an impression that is associated with a characteristic(e.g., a demographic characteristic) of the person exposed to the media.

In the illustrated example, the AME 114 establishes a panel of users whohave agreed to provide their demographic information and to have theirInternet browsing activities monitored. When an individual joins the AMEpanel, the person provides detailed information concerning the person'sidentity and demographics (e.g., gender, age, ethnicity, income, homelocation, occupation, etc.) to the AME 114. The AME 114 sets adevice/user identifier (e.g., an identifier described below inconnection with FIG. 2) on the person's client device 102 that enablesthe AME 114 to identify the panelist.

In the illustrated example, when the AME 114 receives a beacon request108 from a client device 102, the AME 114 requests the client device 102to provide the AME 114 with the device/user identifier the AME 114previously set for the client device 102. The AME 114 uses thedevice/user identifier corresponding to the client device 102 toidentify demographic information in its user AME panelist recordscorresponding to the panelist of the client device 102. In this manner,the AME 114 can generate demographic impressions by associatingdemographic information with an audience impression for the mediaaccessed at the client device 102.

In the illustrated example, three of the client devices 102 a, 102 b,102 c have AME identifiers (IDs) (AME device/user IDs) that identifycorresponding panelists of the AME 114 and also have DP IDs (DPdevice/user IDs) that identify corresponding subscribers of the databaseproprietor 116. In this manner, when the client devices 102 a, 102 b,102 c corresponding to both AME panelists and DP subscribers send beaconrequests 108 to the impression collection entities 104, both the AME 114and the database proprietor 116 can log demographic impressions.(Although for simplicity of illustration, the signaling is not shown inFIG. 1, it is understood that the client devices 102 a, 102 b, 102 c(and/or any other client device) may communicate with the AME 114 and/orthe database proprietor 116 using the redirection mechanism disclosed inMainak et al., U.S. Pat. No. 8,370,489, as described above.) In theillustrated example, the client devices 102 d, 102 e have AME IDs but donot have DP IDs. As such, the database proprietor 116 is unable toidentify the client devices 102 d, 102 e due to those client devices nothaving DP IDs set by the database proprietor 116. The client devices 102d, 102 e may not have DP IDs set by the database proprietor 116 if, forexample, the client devices 102 d, 102 e do not accept cookies, a userdoesn't have an account with the database proprietor 116 or if the userhas an account with the database proprietor 116 but has cleared the DPID (e.g., cleared a cookie cache) and deleted the database proprietor'sDP ID before or at the time of a media exposure. In such instances, ifthe user device 102 is, for example, redirected to contact the databaseproprietor 116 using the system disclosed in Mainak et al., U.S. Pat.No. 8,370,489, the database proprietor 116 is not able to detectdemographics corresponding to the media exposure and, thus, does notreport any audience or impressions for that exposure. In examplesdisclosed herein, the client devices 102 d, 102 e are referred to hereinas client devices over which the database proprietor 116 hasnon-coverage because the database proprietor 116 is unable to identifydemographics corresponding to those client devices 102 d, 102 e. As aresult of the non-coverage, the database proprietor 116 underestimatesthe audience size and number of impressions for corresponding mediaaccessed via the client devices 102 when, for example, operating withinthe system of Mainak et al., U.S. Pat. No. 8,370,489.

FIG. 2 is an example communication flow diagram 200 of an example mannerin which the AME 114 and the DP 116 can collect demographic impressionsbased on client devices 102 reporting impressions to the AME 114 and theDP 116. FIG. 2 also shows an example coverage corrector 202 to correctunique audience sizes and impression counts for impressions reported byclient devices 102 and for which the database proprietor 116 hasnon-coverage of identifiable DP IDs on one or more of those clientdevices 102. The example chain of events shown in FIG. 2 occurs when aclient device 102 accesses media for which the client device 102 reportsan impression to the AME 114 and the database proprietor 116. In someexamples, the client device 102 reports impressions for accessed mediabased on instructions (e.g., beacon instructions) embedded in the mediathat instruct the client device 102 (e.g., instruct a web browser or anapp in the client device 102) to send beacon/impression requests (e.g.,the beacon/impression requests 108 of FIG. 1) to the AME 114 and/or thedatabase proprietor 116. In such examples, the media having the beaconinstructions is referred to as tagged media. In other examples, theclient device 102 reports impressions for accessed media based oninstructions embedded in apps or web browsers that execute on the clientdevice 102 to send beacon/impression requests (e.g., thebeacon/impression requests 108 of FIG. 1) to the AME 114 and/or thedatabase proprietor 116 for corresponding media accessed via those appsor web browsers. In any case, the beacon/impression requests (e.g., thebeacon/impression requests 108 of FIG. 1) include device/useridentifiers (e.g., AME IDs and/or DP IDs) as described further below toallow the corresponding AME 114 and/or the corresponding databaseproprietor 116 to associate demographic information with resultinglogged impressions.

In the illustrated example, the client device 102 accesses media 206that is tagged with the beacon instructions 208. The beacon instructions208 cause the client device 102 to send a beacon/impression request 212to an AME impressions collector 218 when the client device 102 accessesthe media 206. For example, a web browser and/or app of the clientdevice 102 executes the beacon instructions 208 in the media 206 whichinstruct the browser and/or app to generate and send thebeacon/impression request 212. In the illustrated example, the clientdevice 102 sends the beacon/impression request 212 using an HTTP(hypertext transfer protocol) request addressed to the URL (uniformresource locator) of the AME impressions collector 218 at, for example,a first internet domain of the AME 114. The beacon/impression request212 of the illustrated example includes a media identifier 213 (e.g., anidentifier that can be used to identify content, an advertisement,and/or any other media) corresponding to the media 206. In someexamples, the beacon/impression request 212 also includes a siteidentifier (e.g., a URL) of the website that served the media 206 to theclient device 102 and/or a host website ID (e.g., www.acme.com) of thewebsite that displays or presents the media 206. In the illustratedexample, the beacon/impression request 212 includes a device/useridentifier 214. In the illustrated example, the device/user identifier214 that the client device 102 provides to the AME impressions collector218 in the beacon impression request 212 is an AME ID because itcorresponds to an identifier that the AME 114 uses to identify apanelist corresponding to the client device 102. In other examples, theclient device 102 may not send the device/user identifier 214 until theclient device 102 receives a request for the same from a server of theAME 114 in response to, for example, the AME impressions collector 218receiving the beacon/impression request 212.

In some examples, the device/user identifier 214 may be a deviceidentifier (e.g., an international mobile equipment identity (IMEI), amobile equipment identifier (MEID), a media access control (MAC)address, etc.), a web browser unique identifier (e.g., a cookie), a useridentifier (e.g., a user name, a login ID, etc.), an Adobe Flash® clientidentifier, identification information stored in an HTML5 datastore,and/or any other identifier that the AME 114 stores in association withdemographic information about users of the client devices 102. In thismanner, when the AME 114 receives the device/user identifier 214, theAME 114 can obtain demographic information corresponding to a user ofthe client device 102 based on the device/user identifier 214 that theAME 114 receives from the client device 102. In some examples, thedevice/user identifier 214 may be encrypted (e.g., hashed) at the clientdevice 102 so that only an intended final recipient of the device/useridentifier 214 can decrypt the hashed identifier 214. For example, ifthe device/user identifier 214 is a cookie that is set in the clientdevice 102 by the AME 114, the device/user identifier 214 can be hashedso that only the AME 114 can decrypt the device/user identifier 214. Ifthe device/user identifier 214 is an IMEI number, the client device 102can hash the device/user identifier 214 so that only a wireless carrier(e.g., the database proprietor 116) can decrypt the hashed identifier214 to recover the IMEI for use in accessing demographic informationcorresponding to the user of the client device 102. By hashing thedevice/user identifier 214, an intermediate party (e.g., an intermediateserver or entity on the Internet) receiving the beacon request cannotdirectly identify a user of the client device 102.

In response to receiving the beacon/impression request 212, the AMEimpressions collector 218 logs an impression for the media 206 bystoring the media identifier 213 contained in the beacon/impressionrequest 212. In the illustrated example of FIG. 2, the AME impressionscollector 218 also uses the device/user identifier 214 in thebeacon/impression request 212 to identify AME panelist demographicinformation corresponding to a panelist of the client device 102. Thatis, the device/user identifier 214 matches a user ID of a panelistmember (e.g., a panelist corresponding to a panelist profile maintainedand/or stored by the AME 114). In this manner, the AME impressionscollector 218 can associate the logged impression with demographicinformation of a panelist corresponding to the client device 102.

In some examples, the beacon/impression request 212 may not include thedevice/user identifier 214 if, for example, the user of the clientdevice 102 is not an AME panelist. In such examples, the AME impressionscollector 218 logs impressions regardless of whether the client device102 provides the device/user identifier 214 in the beacon/impressionrequest 212 (or in response to a request for the identifier 214). Whenthe client device 102 does not provide the device/user identifier 214,the AME impressions collector 218 will still benefit from logging animpression for the media 206 even though it will not have correspondingdemographics. For example, the AME 114 may still use the loggedimpression to generate a total impressions count and/or a frequency ofimpressions (e.g., an impressions frequency) for the media 206.Additionally or alternatively, the AME 114 may obtain demographicsinformation from the database proprietor 116 for the logged impressionif the client device 102 corresponds to a subscriber of the databaseproprietor 116.

In the illustrated example of FIG. 2, to compare or supplement panelistdemographics (e.g., for accuracy or completeness) of the AME 114 withdemographics from one or more database proprietors (e.g., the databaseproprietor 116), the AME impressions collector 218 returns a beaconresponse message 222 (e.g., a first beacon response) to the clientdevice 102 including an HTTP “302 Found” re-direct message and a URL ofa participating database proprietor 116 at, for example, a secondinternet domain. In the illustrated example, the HTTP “302 Found”re-direct message in the beacon response 222 instructs the client device102 to send a second beacon request 226 to the database proprietor 116.In other examples, instead of using an HTTP “302 Found” re-directmessage, redirects may be implemented using, for example, an iframesource instruction (e.g., <iframe src=“ ”>) or any other instructionthat can instruct a client device to send a subsequent beacon request(e.g., the second beacon request 226) to a participating databaseproprietor 116. In the illustrated example, the AME impressionscollector 218 determines the database proprietor 116 specified in thebeacon response 222 using a rule and/or any other suitable type ofselection criteria or process. In some examples, the AME impressionscollector 218 determines a particular database proprietor to which toredirect a beacon request based on, for example, empirical dataindicative of which database proprietor is most likely to havedemographic data for a user corresponding to the device/user identifier214. In some examples, the beacon instructions 208 include a predefinedURL of one or more database proprietors to which the client device 102should send follow up beacon requests 226. In other examples, the samedatabase proprietor is always identified in the first redirect message(e.g., the beacon response 222).

In the illustrated example of FIG. 2, the beacon/impression request 226may include a device/user identifier 227 that is a DP ID because it isused by the database proprietor 116 to identify a subscriber of theclient device 102 when logging an impression. In some instances (e.g.,in which the database proprietor 116 has not yet set a DP ID in theclient device 102), the beacon/impression request 226 does not includethe device/user identifier 227. In some examples, the DP ID is not sentuntil the database proprietor 116 requests the same (e.g., in responseto the beacon/impression request 226). In some examples, the device/useridentifier 227 is a device identifier (e.g., an international mobileequipment identity (IMEI), a mobile equipment identifier (MEID), a mediaaccess control (MAC) address, etc.), a web browser unique identifier(e.g., a cookie), a user identifier (e.g., a user name, a login ID,etc.), an Adobe Flash® client identifier, identification informationstored in an HTML5 datastore, and/or any other identifier that thedatabase proprietor 116 stores in association with demographicinformation about subscribers corresponding to the client devices 102.When the database proprietor 116 receives the device/user identifier227, the database proprietor 116 can obtain demographic informationcorresponding to a user of the client device 102 based on thedevice/user identifier 227 that the database proprietor 116 receivesfrom the client device 102. In some examples, the device/user identifier227 may be encrypted (e.g., hashed) at the client device 102 so thatonly an intended final recipient of the device/user identifier 227 candecrypt the hashed identifier 227. For example, if the device/useridentifier 227 is a cookie that is set in the client device 102 by thedatabase proprietor 116, the device/user identifier 227 can be hashed sothat only the database proprietor 116 can decrypt the device/useridentifier 227. If the device/user identifier 227 is an IMEI number, theclient device 102 can hash the device/user identifier 227 so that only awireless carrier (e.g., the database proprietor 116) can decrypt thehashed identifier 227 to recover the IMEI for use in accessingdemographic information corresponding to the user of the client device102. By hashing the device/user identifier 227, an intermediate party(e.g., an intermediate server or entity on the Internet) receiving thebeacon request cannot directly identify a user of the client device 102.For example, if the intended final recipient of the device/useridentifier 227 is the database proprietor 116, the AME 114 cannotrecover identifier information when the device/user identifier 227 ishashed by the client device 102 for decrypting only by the intendeddatabase proprietor 116.

Although only a single database proprietor 116 is shown in FIGS. 1 and2, the impression reporting/collection process of FIGS. 1 and 2 may beimplemented using multiple database proprietors. In some such examples,the beacon instructions 208 cause the client device 102 to sendbeacon/impression requests 226 to numerous database proprietors. Forexample, the beacon instructions 208 may cause the client device 102 tosend the beacon/impression requests 226 to the numerous databaseproprietors in parallel or in daisy chain fashion. In some suchexamples, the beacon instructions 208 cause the client device 102 tostop sending beacon/impression requests 226 to database proprietors oncea database proprietor has recognized the client device 102. In otherexamples, the beacon instructions 208 cause the client device 102 tosend beacon/impression requests 226 to database proprietors so thatmultiple database proprietors can recognize the client device 102 andlog a corresponding impression. In any case, multiple databaseproprietors are provided the opportunity to log impressions and providecorresponding demographics information if the user of the client device102 is a subscriber of services of those database proprietors.

In some examples, prior to sending the beacon response 222 to the clientdevice 102, the AME impressions collector 218 replaces site IDs (e.g.,URLs) of media provider(s) that served the media 206 with modified siteIDs (e.g., substitute site IDs) which are discernable only by the AME114 to identify the media provider(s). In some examples, the AMEimpressions collector 218 may also replace a host website ID (e.g.,www.acme.com) with a modified host site ID (e.g., a substitute host siteID) which is discernable only by the AME 114 as corresponding to thehost website via which the media 206 is presented. In some examples, theAME impressions collector 218 also replaces the media identifier 213with a modified media identifier 213 corresponding to the media 206. Inthis way, the media provider of the media 206, the host website thatpresents the media 206, and/or the media identifier 213 are obscuredfrom the database proprietor 116, but the database proprietor 116 canstill log impressions based on the modified values which can later bedeciphered by the AME 114 after the AME 114 receives logged impressionsfrom the database proprietor 116. In some examples, the AME impressionscollector 218 does not send site IDs, host site IDS, the mediaidentifier 213 or modified versions thereof in the beacon response 222.In such examples, the client device 102 provides the original,non-modified versions of the media identifier 213, site IDs, host IDs,etc. to the database proprietor 116.

In the illustrated example, the AME impression collector 218 maintains amodified ID mapping table 228 that maps original site IDs with modified(or substitute) site IDs, original host site IDs with modified host siteIDs, and/or maps modified media identifiers to the media identifierssuch as the media identifier 213 to obfuscate or hide such informationfrom database proprietors such as the database proprietor 116. Also inthe illustrated example, the AME impressions collector 218 encrypts allof the information received in the beacon/impression request 212 and themodified information to prevent any intercepting parties from decodingthe information. The AME impressions collector 218 of the illustratedexample sends the encrypted information in the beacon response 222 tothe client device 102 so that the client device 102 can send theencrypted information to the database proprietor 116 in thebeacon/impression request 226. In the illustrated example, the AMEimpressions collector 218 uses an encryption that can be decrypted bythe database proprietor 116 site specified in the HTTP “302 Found”re-direct message.

Periodically or aperiodically, the impression data collected by thedatabase proprietor 116 is provided to a DP impressions collector 232 ofthe AME 114 as, for example, batch data. As discussed above, the clientdevices 102 d, 102 e of FIG. 1 do not have DP IDs that the databaseproprietor 116 can use to identify demographics for users of thoseclient devices 102. During a data collecting and merging process tocombine demographic and impression data from the AME 114 and thedatabase proprietor(s) 116, impressions logged by the AME 114 for theclient devices 102 d, 102 e will not correspond to impressions logged bythe database proprietor 116 because the database proprietor 116 does notlog impressions for the client devices 102 d, 102 e that do not have DPIDs.

Additional examples that may be used to implement the beacon instructionprocesses of FIG. 2 are disclosed in Mainak et al., U.S. Pat. No.8,370,489, which is hereby incorporated herein by reference in itsentirety. In addition, other examples that may be used to implement suchbeacon instructions are disclosed in Blumenau, U.S. Pat. No. 6,108,637,which is hereby incorporated herein by reference in its entirety.

In the example of FIG. 2, the AME 114 includes the example coveragecorrector 202 to correct unique audience values and impression countsfor impressions reported by client devices 102 for which the databaseproprietor 116 has non-coverage of identifiable DP IDs on one or more ofthose client devices 102. The coverage corrector 202 of the illustratedexample is provided with an example missing-audience (M-A) factordeterminer 234, an example unique audience corrector 236, and an exampleimpressions corrector 238.

The example missing-audience factor determiner 234 of FIG. 2 is providedto calculate a M-A factor representative of the amount of non-coverage(e.g., impressions collected by the AME 114 but not the databaseproprietor 116) relative to an amount of coverage (e.g., impressionscollected by the AME 114 and the database proprietor 116 for which thedatabase proprietor 116 does have coverage). As discussed above,non-coverage occurs when the database proprietor 116 is unable toidentify a client device 102 (e.g., the client devices 102 d, 102 e) dueto such client device not having a device/user identifier (e.g., thedevice/user identifier 227 of FIG. 2) corresponding to a subscriber ofthe database proprietor 116. As also discussed above, coverage occurswhen the database proprietor 116 is able to identify a client device 102(e.g., the client devices 102 d, 102 e) due to such client device havinga device/user identifier (e.g., the device/user identifier 227 of FIG.2) corresponding to a subscriber of the database proprietor 116.

In the illustrated example, the missing-audience factor determiner 234determines M-A factors for different demographic groups based onhistorical impressions (e.g., the example historical impressions of theexample combined impressions table 300 of FIG. 3) collected by the AME114 and the database proprietor 116 based on panelists of the AME 114,some or all of which are also registered subscribers of the databaseproprietor 116. For example, the AME impressions collector 218 can logimpressions of AME panelists in association with known panelistdemographics, and the DP impressions collector 232 can obtain individualdemographic impression records from the database proprietor 116 based onconsent from registered subscribers of the database proprietor 116 thatare also AME panelists. The missing-audience factor determiner 234 canassociate impressions from the database proprietor 116 withcorresponding impressions logged by the AME 114 that correspond to thesame person. In this manner, the missing-audience factor determiner 234can determine AME panelists for which the database proprietor 116 hasnon-coverage (e.g., AME panelists for which the database proprietor 116does not recognize a DP ID and, thus, does not log an impression asshown in the example table 300 of FIG. 3). Using the identifiednon-coverage of the database proprietor 116, the missing-audience factordeterminer 234 can determine M-A factors for different demographicgroups based on the historical impressions.

The example unique audience corrector 236 is provided to correct uniqueaudience sizes or quantities by applying the M-A factor (determined bythe missing-audience factor determiner 234) to total unique audiencesizes corresponding to total impressions collected by the AME 114. Theexample impressions corrector 238 is provided to correct an impressionscount by applying the M-A factor (determined by the missing-audiencefactor determiner 234) to the total number of impressions collected bythe AME 114.

Although the coverage corrector 202 is shown as being located in the AME114, the coverage corrector 202 may alternatively be located anywhereincluding at the database proprietor 116 or at any other suitablelocation separate from the AME 114 and the database proprietor 116. Inaddition, although the AME impressions collector 218, the modified IDmap 228, and the DP impressions collector 232 are shown separate fromthe coverage corrector 202, one or more of the AME impressions collector218, the modified ID map 228, and the DP impressions collector 232 maybe implemented in the coverage corrector 202.

While an example manner of implementing the example coverage corrector202, the example impressions collector 218, the example modified ID map228, the example DP impressions collector 232, the examplemissing-audience factor determiner 234, the example unique audiencecorrector 236, and the example impressions corrector 238 is illustratedin FIG. 2, one or more of the elements, processes and/or devicesillustrated in FIG. 2 may be combined, divided, re-arranged, omitted,eliminated and/or implemented in any other way. Further, the examplecoverage collector 202, the example AME impressions collector 218, theexample modified ID map 228, the example DP impressions collector 232,the example missing-audience factor determiner 234, the example uniqueaudience corrector 236, and/or the example impressions corrector 238 ofFIG. 2 may be implemented by hardware, software, firmware, and/or anycombination of hardware, software and/or firmware. Thus, for example,any of the example coverage collector 202, the example AME impressionscollector 218, the example modified ID map 228, the example DPimpressions collector 232, the example missing-audience factordeterminer 234, the example unique audience corrector 236, and/or theexample impressions corrector 238 could be implemented by one or moreanalog or digital circuit(s), logic circuits, programmable processor(s),application specific integrated circuit(s) (ASIC(s)), programmable logicdevice(s) (PLD(s)), and/or field programmable logic device(s) (FPLD(s)).When reading any of the apparatus or system claims of this patent tocover a purely software and/or firmware implementation, at least one ofthe example coverage collector 202, the example AME impressionscollector 218, the example modified ID map 228, the example DPimpressions collector 232, the example missing-audience factordeterminer 234, the example unique audience corrector 236, and/or theexample impressions corrector 238 is/are hereby expressly defined toinclude a tangible computer readable storage device or storage disk suchas a memory, a digital versatile disk (DVD), a compact disk (CD), aBlu-ray disk, etc. storing the software and/or firmware. Further still,the example coverage corrector 202, the example impressions collector218, the example modified ID map 228, the example DP impressionscollector 232, the example missing-audience factor determiner 234, theexample unique audience corrector 236, and the example impressionscorrector 238 of FIG. 2 may include one or more elements, processesand/or devices in addition to, or instead of, those illustrated in FIG.2, and/or may include more than one of any or all of the illustratedelements, processes and devices.

FIG. 3 illustrates an example combined impressions table 300 based onimpressions collected by the AME 114 and the database proprietor 116 ofFIGS. 1 and 2 for different demographic groups. The impressionscollected by the AME 114 and the database proprietor 116 shown in theexample table 300 are historical impressions that are used to calculateM-A factors for different demographic groups. In the illustratedexample, the database proprietor 116 provides its logged impressionrecords with corresponding DP IDs shown in FIG. 3 to the AME 114 (e.g.,based on consent from AME panelists that are also registered subscribersof the database proprietor 116) so that the AME 114 can determine theM-A factors for the different demographic groups. For example, after themissing-audience factor determiner 234 determines the M-A factors forthe different demographic groups based on the historical impressions ofthe example table 300 of FIG. 3, the M-A factors can be subsequentlyused a number of times on large logs of impressions collected by thedatabase proprietor 116 to correct for the database proprietor'snon-coverage of some client devices that affects accuracies of uniqueaudience sizes and impression counts generated using the databaseproprietor's logged impressions. In some examples, the M-A factors areapplied on aggregated demographic impression data (e.g., not individualimpression records as shown in FIG. 3) generated by the databaseproprietor 116 such as the data shown in example table 400 of FIG. 4.

In the illustrated example of FIG. 3, impression records (IMP #) 1-22correspond to historical impressions collected by both the AME 114 andthe database proprietor 116. That is, each of the impression records(IMP #) 1-22 includes a corresponding AME ID and a corresponding DP ID.In the illustrated example, impression records (IMP #) 23-28 correspondto historical impressions collected by the AME 114 but not the databaseproprietor 116. As such, each of the impression records (IMP #) 23-28includes a corresponding AME ID but does not have a corresponding DP ID.Thus, the impressions (IMP #) 23-28 of FIG. 3 not having DP IDsrecognizable by the database proprietor 116 represent instances of thedatabase proprietor's non-coverage of some client devices. Thenon-coverage represented by the impressions (IMP #) 23-28 of thehistorical impressions of FIG. 3 are representative of proportions ofnon-coverage of client devices that the database proprietor 116 islikely to have for different demographic groups when logging subsequentimpressions for persons that may or may not be AME panelists. Therefore,calculating M-A factors based on the historical impressions of theexample table 300 of FIG. 3 results in M-A factors that can be used tocorrect for the database proprietor's non-coverage of client devices inimpressions subsequently collected by the database proprietor 116.

The non-coverage corresponding to the impressions (IMP #) 23-28 of FIG.3 not having DP IDs recognizable by the database proprietor 116 resultsin inaccurate unique audience values, total impressions, and impressionsfrequency generated by the AME 114 based on the historical loggedimpressions of FIG. 3. That is, because the database proprietor 116 doesnot log impressions (e.g., the impressions 23-28 of FIG. 3) whenrecognizable DP IDs are not located in beacon/impression requests 226(FIG. 2) and/or otherwise provided by the corresponding client device,the total impressions logged by the database proprietor 116 are notcomplete. Examples disclosed herein are useful to adjust/compensateimpressions to overcome such incompleteness of logged impressions due tonon-coverage so that the AME 114 can generate relatively more accurateunique audience values, total impressions, and impressions frequencythan can be achieved using prior techniques when the non-coverage shownin FIG. 3 exists in logged impressions.

In the simplified example impressions table 300 of FIG. 3, all of theimpressions (IMP #) 1-28 are demographic impressions that are associatedwith corresponding demographic groups (e.g., demographic groups definedby age and gender and/or any other demographic criteria). Thedemographic groups associated with each of the impressions areidentified by the AME 114 and/or the database proprietor 116 based ondevice/user identifiers (e.g., the device/user identifier 214 of FIG. 2)and previously collected AME panelist data and/or DP subscriberregistration data.

FIG. 4 illustrates an example table 400 showing demographic-based uniqueaudience values, total impressions values, and impression frequencyvalues based on impressions collected by the database proprietor 116 ofFIGS. 1 and 2 and having inaccuracies due to non-coverage of impressionscorresponding to client devices not having device/user identifiersrecognizable by the database proprietor 116. The demographic groups ofFIG. 4 include females younger than 50 years (F<50), females 50 yearsold and older (F>=50), males younger than 50 years (M<50), and males 50years old and older (M>=50). The example demographic-based uniqueaudience values, total impressions values, and impression frequencyvalues of the example table 400 of FIG. 4 are determined usingimpressions collected by the database proprietor 116 based on clientdevices corresponding to subscribers of the database proprietor 116regardless of whether those subscribers are also panelist members of theAME 114. That is, unlike the impressions shown in the example table 300of FIG. 3 that corresponded to AME panelists (of which some or all arealso database proprietor subscribers) and which are used by themissing-audience factor determiner 234 to determine M-A factors fordifferent demographic groups, the database proprietor impressions usedto generate the data in the example table 400 of FIG. 4 are logged bythe database proprietor 116 regardless of whether correspondingsubscribers of those impressions are AME panelists. This is because theimpressions of the example table 300 of FIG. 3 are used by themissing-audience factor determiner 234 to generate M-A factors (e.g.,M-A factors which are based on correlating true, known demographics ofthe AME panelists with corresponding impressions collected by thedatabase proprietor 116 for those same AME panelists) that can besubsequently used to correct subsequently collected impression-baseddata (e.g., the impression-based data of the example table 400 of FIG.4) for database proprietor non-coverage due to not having DP IDs set onsome client devices.

In the illustrated example of FIG. 4, the database proprietor aggregatedemographic impression-based data of the table 400 is aggregate datagenerated by the database proprietor 116 of FIGS. 1 and 2. In theillustrated example, the database proprietor 116 provides the AME 114with such aggregate demographic impression-based data to protect theprivacies of database proprietor subscribers. That is, by not providingindividual impression records such as shown in the example table 300 ofFIG. 3, the database proprietor 116 does not reveal the identities ofits subscribers to the AME 114, thereby protecting the privacies of itssubscribers. In other examples, when database proprietor subscribersconsent to sharing their demographic information and identities withthird parties, the database proprietor 116 may share individualimpression records (such as the impression records of the example table300 of FIG. 3) with the AME 114. In such examples, the AME 114 maygenerate aggregate demographic impression-based data (such as the datashown in FIG. 4) based on the individual impression records provided bythe database proprietor 116.

In the illustrated example of FIG. 4, the database proprietor 116 usesits logged impressions to determine an example database proprietorunique audience size (DP_UA) or database proprietor unique audiencequantity 402, example database proprietor total impressions (DP_IMP)404, and an example database proprietor impressions frequency (DP_FREQ)406 of the illustrated example of FIG. 4. In the illustrated example,the database proprietor unique audience quantity 402, the databaseproprietor total impressions (DP_IMP) 404, and the database proprietorimpressions frequency (DP_FREQ) 406 are measures that correspond todatabase proprietor subscribers. As such, the database proprietor uniqueaudience quantity 402 is a unique audience size of database proprietorsubscribers), the example database proprietor total impressions (DP_IMP)404 are total impressions corresponding to database proprietorsubscribers, and the example database proprietor impressions frequency(DP_FREQ) 406 is an impressions frequency corresponding to databaseproprietor subscribers. In other examples the database proprietor 116may additionally or alternatively determine any otheraudience/impression characteristic based on the data collected from thedatabase proprietor 116. In examples in which the database proprietor116 provides individual impression records to the AME 114, the DPimpressions collector 232 (FIG. 2) may process the individual impressionrecords to generate the database proprietor unique audience size (DP_UA)402, the database proprietor total impressions (DP_IMP) 404, and thedatabase proprietor impressions frequency (DP_FREQ) 406 of FIG. 4.

As used herein, a unique audience measure (e.g., the database proprietorunique audience size (DP_UA) 402 of FIG. 4) is based on audience membersdistinguishable from one another. That is, a particular audience memberexposed to particular media is measured as a single unique audiencemember regardless of how many times that audience member is exposed tothat particular media. If that particular audience member is exposedmultiple times to the same media, the multiple exposures for theparticular audience member to the same media is counted as only a singleunique audience member. In this manner, impression performance forparticular media is not disproportionately represented when a smallsubset of one or more audience members is exposed to the same media anexcessively large number of times while a larger number of audiencemembers is exposed fewer times or not at all to that same media. Bytracking exposures to unique audience members, a unique audience measuremay be used to determine a reach measure to identify how many uniqueaudience members are reached by media. In some examples, increasingunique audience and, thus, reach, is useful for advertisers wishing toreach a larger audience base.

The database proprietor unique audience size (DP_UA) 402 of FIG. 4represents a number of database proprietor subscribers (e.g., peopleregistered or subscribed to use the services of the database proprietor116) exposed to particular media. In the illustrated example, thedatabase proprietor 116 and/or the DP impressions collector 232 of FIG.2 determine(s) the database proprietor unique audience size (DP_UA) 402based on impressions logged by the database proprietor 116 for clientdevices 102 that do have DP IDs without counting more than oneimpression logged for each unique registered subscriber of the databaseproprietor 116.

As used herein, total impressions (e.g., the database proprietorimpressions (DP_IMP) 404 of FIG. 4) refers to the total number ofcollected impressions for particular media regardless of whethermultiple ones of those impressions are attributable to the same audiencemembers. That is, multiple impressions accounted for in the totalimpressions may be attributable to a same audience member. The databaseproprietor impressions (DP_IMP) 404 of FIG. 4 are total impressionslogged by the database proprietor 116 for particular media accessed viaclient devices 102 that have DP IDs recognizable by the databaseproprietor 116 to identify a subscriber registered with the databaseproprietor 116.

As used herein, impressions frequency (e.g., the database proprietorfrequency (DP_FREQ) 408 of FIG. 4) is a number of total impressions(e.g., the database proprietor impressions (DP_IMP) 404 of FIG. 4)divided by a quantity of unique audience members (e.g., the databaseproprietor unique audience size (DP_UA) 402 of FIG. 4) (e.g., DP_FREQ=DP_IMP/DP_UA). For example, for the demographic group of femalesyounger than 50 (F<50), the database proprietor frequency (DP_FREQ) is3.1, which is calculated by dividing 261,644 total impressions (DP_IMP)by 83,758 unique audience (DP_UA).

FIG. 5 is an example table 500 showing example missing-audience (M-A)factors 502 calculated by the coverage corrector 202 of FIG. 2 for thedifferent demographic groups represented in the table 500 of FIG. 5. TheM-A factors 502 of the illustrated example are determined using Equation1 below.

M-A Factor_((DEMO GP))=(DP non-coverage quantity)_((DEMO GP))/(DPcoverage quantity)_((DEMO GP))   Equation 1

In Equation 1 above, the “DP non-coverage quantity” is a quantity ofunique audience members in a demographic group (DEMO GP) that do nothave DP device and/or user identifiers on their client devices. That is,the “DP non-coverage quantity” is a number of unique audience members(e.g., a unique audience count) for a demographic group (DEMO GP) forwhich impressions were logged by the AME 114 for client devices 102(FIG. 1) that do not have a device and/or user identifier (DP ID)recognizable by the database proprietor 116. Also in Equation 1 above,the “DP coverage quantity” is a quantity of unique audience members in ademographic group (DEMO GP) that have DP device and/or user identifierson their client devices. That is, the “DP coverage quantity” is a numberof unique audience members (e.g., a unique audience count) for thedemographic group (DEMO GP) for which impressions were logged by thedatabase proprietor 116 based on client devices 102 having a deviceand/or user identifier (DP ID) recognizable by the database proprietor116. Thus, according to Equation 1 above, an M-A factor 502 of FIG. 5for a particular demographic group is determined by obtaining a uniqueaudience count (e.g., a number of unique audience members) for whichimpressions were logged by the AME 114 for client devices 102 (FIG. 1)that do not have a device/user identifier (DP ID) recognizable by thedatabase proprietor 116 (e.g., a DP non-coverage quantity), and dividingthat unique audience count by the number of unique audience members(e.g., another unique audience count) for which impressions were loggedby the database proprietor 116 based on client devices 102 having adevice/user identifier (DP ID) recognizable by the database proprietor116 (e.g., a DP coverage quantity).

For example, the M-A factor 502 for the demographic group F<50 is 67%,which the missing-audience factor determiner 234 determines based onimpression records (IMP #) 3, 7, 11, 26, and 27 of FIG. 3 belonging tothe F<50 demographic group. In the illustrated example of FIG. 3, themissing-audience factor determiner 234 determines that impressionrecords (IMP #) 3, 7, and 11 correspond to client devices 102 having DPIDs. For example, impression record 3 corresponds to DP ID number 2,impression record 7 corresponds to DP ID number 4, and impression record11 corresponds to DP ID number 8. Also in the illustrated example ofFIG. 3, impression records 26 and 27 correspond to client devices 102that do not have DP IDs. As such, the database proprietor 116 hascoverage for impression records 3, 7, and 11 but has non-coverage forimpression records 26 and 27. In the illustrated example, themissing-audience factor determiner 234 determines that the coverageimpression numbers 3, 7, and 11 correspond to the “DP coverage quantity”of Equation 1. In addition, the missing-audience factor determiner 234determines that the non-coverage impression records 26 and 27 correspondto the “DP non-coverage quantity” of Equation 1. As such, in thisexample, the missing-audience factor determiner 234 determines that twois the “DP non-coverage quantity,” and that three is the “DP coveragequantity.” Thus, based on Equation 1 above, the missing-audience factordeterminer 234 determines that the M-A factor_((DEMO GP)) is 67% (e.g.,M-A factor_((DEMO GP))=⅔) for the F<50 demographic group.

FIG. 6 is an example table 600 showing example coverage-corrected uniqueaudience (CCUA) values 602 and example coverage-corrected impression(CCI) counts 604 for different demographic groups based on the M-Afactors 502 of FIG. 5. In the illustrated example, the unique audiencecorrector 236 of FIG. 2 determines the coverage-corrected uniqueaudience values 602 using Equation 2 below.

CCUA=DP_UA+(M-A factor×DP_UA)   Equation 2

In Equation 2 above, the unique audience corrector 236 determines thecoverage-corrected unique audience (CCUA) 602 by adding the databaseproprietor unique audience (DP_UA) value to the product of the M-Afactor and the database proprietor unique audience (DP_UA) value. Forexample, the unique audience corrector 236 calculates thecoverage-corrected unique audience (CCUA) 602 for the demographic groupF<50 by multiplying 67% (the M-A factor of FIG. 5 for the demographicgroup F<50) by 83,758 (the DP_UA of FIG. 4 for the demographic groupF<50) to generate the product of 56,117 (M-A factor×DP_UA). The exampleunique audience corrector 236 then adds the resulting product (55,838)to 83,758 (the DP_UA of FIG. 4) to generate the coverage-correctedunique audience (CCUA) of 139,597. Thus, the unique audience corrector236 adjusts the coverage-corrected unique audience (CCUA) based onEquation 2 above to reflect a larger quantity of unique audience membersto compensate for the non-coverage of client devices 102 that do nothave a DP ID recognizable by the database proprietor 116. That is, thecoverage-corrected unique audience (CCUA) sizes 602 of FIG. 6 are largerthan the database proprietor unique audience (DP_UA) sizes 402 of FIG. 4because the coverage-corrected unique audience (CCUA) sizes 602 areadjusted to correspond to quantities of impressions logged by thedatabase proprietor 116 for client devices 102 that do have a DP IDrecognizable by the database proprietor 116 and quantities ofimpressions not logged by the database proprietor 116 due to clientdevices 102 not having a DP ID recognizable by the database proprietor116.

In the illustrated example of FIG. 6, impressions corrector 238 of FIG.2 determines the coverage-corrected impressions (CCI) 604 using Equation3 below.

CCI=CCUA×DP_FREQ   Equation 3

In Equation 3 above, the impressions corrector 238 determines thecoverage-corrected impressions (CCI) 604 by multiplying thecoverage-corrected unique audience (CCUA) value by the databaseproprietor frequency (DP_FREQ). For example, the impressions corrector238 calculates the coverage-corrected impressions (CCI) 604 for thedemographic group F<50 by multiplying 139,597 (the CCUA for thedemographic group F<50) by 3.1 (the DP_FREQ of FIG. 5 for thedemographic group F<50). The resulting coverage-corrected impressions(CCI) is 436,074. Thus, the impressions corrector 238 adjusts thecoverage-corrected impressions (CCI) based on Equation 3 above toreflect a larger quantity of impressions to compensate for thenon-coverage of client devices 102 that do not have a DP ID recognizableby the database proprietor 116. That is, the coverage-correctedimpressions (CCI) 604 of FIG. 6 are larger than the database proprietorimpressions (DP_IMP) 404 of FIG. 4 because the coverage-correctedimpressions (CCI) 604 represent quantities of impressions logged by thedatabase proprietor 116 for client devices 102 that do have a DP IDrecognizable by the database proprietor 116 and quantities ofimpressions not logged by the database proprietor 116 due to clientdevices 102 not having a DP ID recognizable by the database proprietor116.

FIG. 7 is a flow diagram representative of machine readable instructionsthat may be executed to implement the coverage corrector 202 of FIG. 2to determine the M-A factors 502 of FIG. 5, the coverage-correctedunique audience (CCUA) 602 of FIG. 6, and the coverage-correctedimpressions (CCI) 604 of FIG. 6. In this example, the machine readableinstructions comprise a program for execution by a processor such as theprocessor 812 shown in the example processor platform 800 discussedbelow in connection with FIG. 8. The program may be embodied in softwarestored on a tangible computer readable storage medium such as a CD-ROM,a floppy disk, a hard drive, a digital versatile disk (DVD), a Blu-raydisk, or a memory associated with the processor 812, but the entireprogram and/or parts thereof could alternatively be executed by a deviceother than the processor 812 and/or embodied in firmware or dedicatedhardware. Further, although the example program is described withreference to the flowchart illustrated in FIG. 7, many other methods ofimplementing the example coverage corrector 202 may alternatively beused. For example, the order of execution of the blocks may be changed,and/or some of the blocks described may be changed, eliminated, orcombined.

As mentioned above, the example process of FIG. 7 may be implementedusing coded instructions (e.g., computer and/or machine readableinstructions) stored on a tangible computer readable storage medium suchas a hard disk drive, a flash memory, a read-only memory (ROM), acompact disk (CD), a digital versatile disk (DVD), a cache, arandom-access memory (RAM) and/or any other storage device or storagedisk in which information is stored for any duration (e.g., for extendedtime periods, permanently, for brief instances, for temporarilybuffering, and/or for caching of the information). As used herein, theterm tangible computer readable storage medium is expressly defined toinclude any type of computer readable storage device and/or storage diskand to exclude propagating signals and to exclude transmission media. Asused herein, “tangible computer readable storage medium” and “tangiblemachine readable storage medium” are used interchangeably. Additionallyor alternatively, the example process of FIG. 7 may be implemented usingcoded instructions (e.g., computer and/or machine readable instructions)stored on a non-transitory computer and/or machine readable medium suchas a hard disk drive, a flash memory, a read-only memory, a compactdisk, a digital versatile disk, a cache, a random-access memory and/orany other storage device or storage disk in which information is storedfor any duration (e.g., for extended time periods, permanently, forbrief instances, for temporarily buffering, and/or for caching of theinformation). As used herein, the term non-transitory computer readablemedium is expressly defined to include any type of computer readablestorage device and/or storage disk and to exclude propagating signalsand to exclude transmission media. As used herein, when the phrase “atleast” is used as the transition term in a preamble of a claim, it isopen-ended in the same manner as the term “comprising” is open ended.

The example flow diagram of FIG. 7 is shown as two phases including anexample M-A factors development phase 702 and an example non-coveragecorrection phase 704. During the M-A factors development phase 702, themissing-audience factor determiner 234 (FIG. 2) determines the M-Afactors 502 (FIG. 5) for different demographic groups based onhistorical impressions such as the historical impressions shown in table300 of FIG. 3. During the non-coverage correction phase 704, the uniqueaudience corrector 236 uses the M-A factors 502 to determinecoverage-corrected unique audience sizes (e.g., the coverage-correctedUA (CCUA) 602 of FIG. 6) for different demographic groups. Also duringthe non-coverage correction phase 704, the impressions corrector 238determines coverage-corrected impression counts (e.g., thecoverage-corrected impressions (CCI) 604 of FIG. 6) for differentdemographic groups. In some examples, the non-coverage correction phase704 may begin immediately after the M-A factors development phase 702.In other examples, the non-coverage correction phase 704 may begin aftera significant amount of time (e.g., hours, days, weeks, etc.) has passedfollowing the completion of the M-A factors development phase 702. Insome examples, the M-A factors development phase 702 and thenon-coverage correction phase 704 may be implemented as part of a sameprogram. In other examples, the M-A factors development phase 702 andthe non-coverage correction phase 704 may be implemented as two separateprograms.

The example M-A factors development phase 702 of FIG. 7 begins at block706 at which the AME impressions collector 218 and the DP impressionscollector 232 collect historical impressions corresponding to clientdevices 102. For example, the AME impressions collector 218 collectsimpressions using the techniques described above in connection with FIG.2, and the DP impressions collector 232 obtains impression recordscollected by the database proprietor 116 using the techniques describedabove in connection with FIG. 2.

The example missing-audience factor determiner 234 selects a demographicgroup (block 708). For example, the missing-audience factor determiner234 selects one of the demographic groups of FIGS. 4-6. The examplemissing-audience factor determiner 234 determines a DP coverage amountand a DP non-coverage amount for the selected demographic group based onthe historical impressions (block 710). For example, referring to theexample table 300 of FIG. 3, the missing-audience factor determiner 234determines the DP coverage amount for the selected demographic groupbased on impression records (IMP#) 1-22 and the DP non-coverage amountfor the selected demographic group based on impression records (IMP #)23-28.

The example missing-audience factor determiner 234 determines a M-Afactor 502 (FIG. 5) for the selected demographic group (block 712). Forexample, the missing-audience factor determiner 234 (FIG. 2) determinesthe M-A factor 502 as described above using Equation 1 based on the DPnon-coverage quantity and the DP coverage quantity for the selecteddemographic group determined at block 710. The example missing-audiencefactor determiner 234 determines whether there is another demographicgroup for which to determine an M-A factor (block 714). If there isanother demographic group for which to determine an M-A factor 502,control returns to block 708 at which another demographic group isselected. If there is not another demographic group for which todetermine an M-A factor 502, The M-A factors development phase 702 ends.In some examples, after the M-A factors development phase 702 ends,control advances immediately to the non-coverage correction phase 704 tocorrect for non-coverage errors in impressions collected by the databaseproprietor 116. In other examples, after the M-A factors developmentphase 702 ends, control advances to the non-coverage correction phase704 after some time elapses (e.g., hours, days, weeks, etc.).

In the non-coverage correction phase 704 of the illustrated example, theDP impressions collector 232 obtains database proprietor aggregatedemographic impression-based data (block 716). For example, the DPimpressions collector 232 obtains the database proprietor uniqueaudience (DP_UA) values 402 (FIG. 4), the database proprietorimpressions (DP_IMP) 404 (FIG. 4), and the database proprietor frequency(DP_FREQ) 406 (FIG. 4) from the database proprietor 116. For example,the database proprietor 116 logs impressions from client devices 102 asdescribed above in connection with FIG. 2 and determines the databaseproprietor unique audience (DP_UA) values 402, the database proprietorimpressions (DP_IMP) 404, and the database proprietor frequency(DP_FREQ) 406 based on the logged impressions. In this manner, thedatabase proprietor 116 can provide the database proprietor aggregatedemographic impression-based data 402, 404, 406 to the DP impressionscollector 232. Alternatively, in some examples the DP impressionscollector 232 determines the database proprietor unique audience (DP_UA)values 402 (FIG. 4), the database proprietor impressions (DP_IMP) 404(FIG. 4), and the database proprietor frequency (DP_FREQ) 406 (FIG. 4)based on impression records obtained from the database proprietor 116.

The example coverage corrector 202 selects a demographic group (block718). For example, the coverage corrector 202 selects one of thedemographic groups of FIGS. 4-6. The unique audience corrector 236 (FIG.2) determines the coverage-corrected unique audience (CCUA) 602 (FIG. 6)(block 720). For example, the unique audience corrector 236 candetermine the CCUA 602 based on Equation 2 as described above based onthe M-A factor 502 determined in block 712 for the selected demographicgroup. The impressions corrector 238 (FIG. 2) determines thecoverage-corrected impressions (CCI) 604 (FIG. 6) (block 722). Forexample, the described above based on the M-A factor 502 determined inblock 712 for the selected demographic group.

The coverage corrector 202 determines whether there is anotherdemographic group for which to determine a coverage-corrected uniqueaudience (CCUA) value 602 and/or a coverage-corrected impressions (CCI)value 604 (block 724). If there is another demographic group for whichto determine a coverage-corrected unique audience (CCUA) value 602and/or a coverage-corrected impressions (CCI) value 604, control returnsto block 718 at which another demographic group is selected. Otherwise,if there is not another demographic group for which to determine acoverage-corrected unique audience (CCUA) 602 and/or acoverage-corrected impressions (CCI) 604, the example process of FIG. 7ends.

FIG. 8 is a block diagram of an example processor platform 800 capableof executing the instructions of FIG. 7 to implement the coveragecorrector 202 of FIG. 2. The processor platform 800 can be, for example,a server, a personal computer, or any other type of computing device.

The processor platform 800 of the illustrated example includes aprocessor 812. The processor 812 of the illustrated example is hardware.For example, the processor 812 can be implemented by one or moreintegrated circuits, logic circuits, microprocessors or controllers fromany desired family or manufacturer.

The processor 812 of the illustrated example includes a local memory 813(e.g., a cache). The processor 812 of the illustrated example is incommunication with a main memory including a volatile memory 814 and anon-volatile memory 816 via a bus 818. The volatile memory 814 may beimplemented by Synchronous Dynamic Random Access Memory (SDRAM), DynamicRandom Access Memory (DRAM), RAMBUS Dynamic Random Access Memory (RDRAM)and/or any other type of random access memory device. The non-volatilememory 816 may be implemented by flash memory and/or any other desiredtype of memory device. Access to the main memory 814, 816 is controlledby a memory controller.

The processor platform 800 of the illustrated example also includes aninterface circuit 820. The interface circuit 820 may be implemented byany type of interface standard, such as an Ethernet interface, auniversal serial bus (USB), and/or a PCI express interface.

In the illustrated example, one or more input devices 822 are connectedto the interface circuit 820. The input device(s) 822 permit(s) a userto enter data and commands into the processor 812. The input device(s)can be implemented by, for example, an audio sensor, a microphone, acamera (still or video), a keyboard, a button, a mouse, a touchscreen, atrack-pad, a trackball, isopoint and/or a voice recognition system.

One or more output devices 824 are also connected to the interfacecircuit 820 of the illustrated example. The output devices 824 can beimplemented, for example, by display devices (e.g., a light emittingdiode (LED), an organic light emitting diode (OLED), a liquid crystaldisplay, a cathode ray tube display (CRT), a touchscreen, a tactileoutput device, a light emitting diode (LED), a printer and/or speakers).The interface circuit 820 of the illustrated example, thus, typicallyincludes a graphics driver card, a graphics driver chip or a graphicsdriver processor.

The interface circuit 820 of the illustrated example also includes acommunication device such as a transmitter, a receiver, a transceiver, amodem and/or network interface card to facilitate exchange of data withexternal machines (e.g., computing devices of any kind) via a network826 (e.g., an Ethernet connection, a digital subscriber line (DSL), atelephone line, coaxial cable, a cellular telephone system, etc.).

The processor platform 800 of the illustrated example also includes oneor more mass storage devices 828 for storing software and/or data.Examples of such mass storage devices 828 include floppy disk drives,hard drive disks, compact disk drives, Blu-ray disk drives, RAIDsystems, and digital versatile disk (DVD) drives.

Coded instructions 832 include the machine readable instructions of FIG.7 and may be stored in the mass storage device 828, in the volatilememory 814, in the non-volatile memory 816, and/or on a removabletangible computer readable storage medium such as a CD or DVD.

From the foregoing, it will be appreciated that above disclosed methods,apparatus and articles of manufacture are useful to enhance theoperations of a computer to improve the accuracy of impression-baseddata such as unique audience and impression counts so that computers andprocessing systems therein can be relied upon to produce audienceanalysis information with higher accuracies. In some examples, computeroperations can be made more efficient based on the above equations fordetermining M-A factors, coverage-corrected unique audience (CCUA), andcoverage-corrected impressions (CCI). That is, through the use of theseprocesses, computers can operate more efficiently by using fewerprocessor cycles to relatively quickly identify parameter values neededto determine coverage-corrected data and applying those parameter valuesthrough the above equations to determine the coverage-corrected data.Such coverage-corrected data is useful in subsequent processing foridentifying exposure performances of different media so that mediaproviders, advertisers, product manufacturers, and/or service providerscan make more informed decisions on how to spend advertising dollarsand/or media production and distribution dollars. Furthermore, examplemethods, apparatus, and/or articles of manufacture disclosed hereinovercome the technical problem of counting impressions of media on mediadevices which do not have a user/device identifier such as cookiesand/or other identifiers. Example methods, apparatus, and/or articles ofmanufacture disclosed herein solve this problem without forcing suchdevices to store user/device identifiers and without requiring follow-upnetwork communications with the client device. This solution, thus,avoids creating additional network traffic and further avoids therequirement to store additional data such as identifiers at clientdevices.

Although certain example methods, apparatus and articles of manufacturehave been disclosed herein, the scope of coverage of this patent is notlimited thereto. On the contrary, this patent covers all methods,apparatus and articles of manufacture fairly falling within the scope ofthe claims of this patent.

What is claimed is:
 1. An apparatus comprising: at least one memory;instructions in the apparatus; and processor circuitry to execute theinstructions to: access a unique audience size of database proprietorsubscribers exposed to media, the unique audience size of the databaseproprietor subscribers generated by a process of a server of a databaseproprietor based on a first quantity of impressions, the first quantityof impressions corresponding to first client devices that includedatabase proprietor identifiers, the first quantity of impressionsexclusive of a second quantity of impressions corresponding to secondclient devices that do not include the database proprietor identifiers;and apply a missing-audience factor to the unique audience size of thedatabase proprietor subscribers exposed to the media to produce acoverage-corrected unique audience size that corrects the uniqueaudience size generated by the server of the database proprietor byusing the coverage-corrected unique audience size to represent the firstand second client devices in place of the unique audience size thatcorresponds to the first client devices exclusive of the second clientdevices.
 2. The apparatus of claim 1, wherein the processor circuitry isto execute the instructions to determine a coverage-corrected impressioncount based on the coverage-corrected unique audience size and animpressions frequency, the coverage-corrected impression countrepresentative of the first quantity of impressions corresponding to thefirst client devices and the second quantity of impressionscorresponding to the second client devices.
 3. The apparatus of claim 2,wherein the impressions frequency is the first quantity of impressionsdivided by the unique audience size of the database proprietorsubscribers.
 4. The apparatus of claim 1, wherein the missing-audiencefactor is based on a third quantity of the impressions divided by afourth quantity of the impressions, the third quantity of theimpressions corresponding to the second client devices that do notinclude the database proprietor identifiers, and the fourth quantity ofthe impressions corresponding to the first client devices that includethe database proprietor identifiers.
 5. The apparatus of claim 1,wherein the processor circuitry is operated by the database proprietor.6. The apparatus of claim 1, wherein the database proprietor is at leastone of a social network service provider or an email service provider.7. The apparatus of claim 1, wherein the processor circuitry is toexecute the instructions to log the first quantity of impressions at theserver of the database proprietor based on redirect networkcommunications that instruct the first and second client devices tocommunicate with the server of the database proprietor.
 8. The apparatusof claim 1, wherein the processor circuitry is to execute theinstructions to determine the missing-audience factor based on the firstquantity of impressions logged by the server for the first clientdevices, and based on the second quantity of impressions logged by theserver for the second client devices, the first and second quantities ofimpressions indicative of accesses to media at the first and secondclient devices.
 9. A non-transitory computer readable storage mediumcomprising instructions that, when executed, cause at least oneprocessor to at least: access a unique audience size of databaseproprietor subscribers exposed to media, the unique audience size of thedatabase proprietor subscribers generated by a process of a server of adatabase proprietor based on a first quantity of impressions, the firstquantity of impressions corresponding to first client devices thatinclude database proprietor identifiers, the first quantity ofimpressions exclusive of a second quantity of impressions correspondingto second client devices that do not include the database proprietoridentifiers; and apply a missing-audience factor to the unique audiencesize of the database proprietor subscribers exposed to the media toproduce a coverage-corrected unique audience size that corrects theunique audience size generated by the server of the database proprietorby using the coverage-corrected unique audience size to represent thefirst and second client devices in place of the unique audience sizethat corresponds to the first client devices exclusive of the secondclient devices.
 10. The non-transitory computer readable storage mediumof claim 9, wherein the instructions are further to cause the at leastone processor to determine a coverage-corrected impression count basedon the coverage-corrected unique audience size and an impressionsfrequency, the coverage-corrected impression count representative of thefirst quantity of impressions corresponding to the first client devicesand the second quantity of impressions corresponding to the secondclient devices.
 11. The non-transitory computer readable storage mediumof claim 10, wherein the impressions frequency is the first quantity ofimpressions divided by the unique audience size of the databaseproprietor subscribers.
 12. The non-transitory computer readable storagemedium of claim 9, wherein the missing-audience factor is based on athird quantity of the impressions divided by a fourth quantity of theimpressions, the third quantity of the impressions corresponding to thesecond client devices that do not include the database proprietoridentifiers, and the fourth quantity of the impressions corresponding tothe first client devices that include the database proprietoridentifiers.
 13. The non-transitory computer readable storage medium ofclaim 9, wherein the database proprietor is at least one of a socialnetwork service provider or an email service provider.
 14. Thenon-transitory computer readable storage medium of claim 9, wherein theinstructions are further to cause the at least one processor to log thefirst quantity of impressions at the server of the database proprietorbased on redirect network communications that instruct the first andsecond client devices to communicate with the server of the databaseproprietor.
 15. The non-transitory computer readable storage medium ofclaim 9, wherein the instructions are further to cause the at least oneprocessor to determine the missing-audience factor based on the firstquantity of impressions logged by the server for the first clientdevices, and based on the second quantity of impressions logged by theserver for the second client devices, the first and second quantities ofimpressions indicative of accesses to media at the first and secondclient devices.
 16. A method comprising: accessing, by executing aninstruction with a processor, a unique audience size of databaseproprietor subscribers exposed to media, the unique audience size of thedatabase proprietor subscribers generated by a process of a server of adatabase proprietor based on a first quantity of impressions, the firstquantity of impressions corresponding to first client devices thatinclude database proprietor identifiers, the first quantity ofimpressions exclusive of a second quantity of impressions correspondingto second client devices that do not include the database proprietoridentifiers; and applying, by executing an instruction with theprocessor, a missing-audience factor to the unique audience size of thedatabase proprietor subscribers exposed to the media to produce acoverage-corrected unique audience size that corrects the uniqueaudience size generated by the server of the database proprietor byusing the coverage-corrected unique audience size to represent the firstand second client devices in place of the unique audience size thatcorresponds to the first client devices exclusive of the second clientdevices.
 17. The method of claim 16, further including determining acoverage-corrected impression count based on the coverage-correctedunique audience size and an impressions frequency, thecoverage-corrected impression count representative of the first quantityof impressions corresponding to the first client devices and the secondquantity of impressions corresponding to the second client devices. 18.The method of claim 17, wherein the impressions frequency is the firstquantity of impressions divided by the unique audience size of thedatabase proprietor subscribers.
 19. The method of claim 16, wherein themissing-audience factor is based on a third quantity of the impressionsdivided by a fourth quantity of the impressions, the third quantity ofthe impressions corresponding to the second client devices that do notinclude the database proprietor identifiers, and the fourth quantity ofthe impressions corresponding to the first client devices that includethe database proprietor identifiers.
 20. The method of claim 16, whereinthe database proprietor is at least one of a social network serviceprovider or an email service provider.
 21. The method of claim 16,further including logging the first quantity of impressions at theserver of the database proprietor based on redirect networkcommunications that instruct the first and second client devices tocommunicate with the server of the database proprietor.
 22. The methodof claim 16, further including determining the missing-audience factorbased on the first quantity of impressions logged by the server for thefirst client devices, and based on the second quantity of impressionslogged by the server for the second client devices, the first and secondquantities of impressions indicative of accesses to media at the firstand second client devices.
 23. An apparatus comprising: at least onememory; and processor circuitry including one or more of: at least oneof a central processing unit, a graphic processing unit or a digitalsignal processor, the at least one of the central processing unit, thegraphic processing unit or the digital signal processor having controlcircuitry to control data movement within the processor circuitry,arithmetic and logic circuitry to perform one or more first operationscorresponding to instructions, and one or more registers to store aresult of the one or more first operations, the instructions in theapparatus; a Field Programmable Gate Array (FPGA), the FPGA includinglogic gate circuitry, a plurality of configurable interconnections, andstorage circuitry, the logic gate circuitry and interconnections toperform one or more second operations, the storage circuitry to store aresult of the one or more second operations; or Application SpecificIntegrate Circuitry including logic gate circuitry to perform one ormore third operations; the processor circuitry to at least one ofperform at least one of the first operations, the second operations orthe third operations to: access a unique audience size of databaseproprietor subscribers exposed to media, the unique audience size of thedatabase proprietor subscribers generated by a process of a server of adatabase proprietor based on a first quantity of impressions, the firstquantity of impressions corresponding to first client devices thatinclude database proprietor identifiers, the first quantity ofimpressions exclusive of a second quantity of impressions correspondingto second client devices that do not include the database proprietoridentifiers; and apply a missing-audience factor to the unique audiencesize of the database proprietor subscribers exposed to the media toproduce a coverage-corrected unique audience size that corrects theunique audience size generated by the server of the database proprietorby using the coverage-corrected unique audience size to represent thefirst and second client devices in place of the unique audience sizethat corresponds to the first client devices exclusive of the secondclient devices.